public class MutateAllNodesPipeline extends GPBreedingPipeline
MutateAllNodesPipeline chooses a subtree and for each node n in that subtree, it replaces n with a randomly-picked node of the same arity and type constraints. Thus the original topological structure is the same but the nodes are different.
Typical Number of Individuals Produced Per produce(...) call
...as many as the source produces
Number of Sources
1
Parameters
| base.ns.0 classname, inherits and != GPNodeSelector |
(GPNodeSelector for tree) |
| base.tree.0 0 < int < (num trees in individuals), if exists |
(tree chosen for mutation; if parameter doesn't exist, tree is picked at random) |
Default Base
gp.breed.mutate-all-nodes
Parameter bases
| base.ns | The GPNodeSelector selector |
| Modifier and Type | Field and Description |
|---|---|
GPNodeSelector |
nodeselect
How the pipeline chooses a subtree to mutate
|
static int |
NUM_SOURCES |
static java.lang.String |
P_MUTATEALLNODES |
P_NODESELECTOR, P_TREE, TREE_UNFIXEDDYNAMIC_SOURCES, likelihood, mybase, P_LIKELIHOOD, P_NUMSOURCES, P_SOURCE, sources, V_SAMENO_PROBABILITY, P_PROB, probability| Constructor and Description |
|---|
MutateAllNodesPipeline() |
| Modifier and Type | Method and Description |
|---|---|
java.lang.Object |
clone()
Creates a new individual cloned from a prototype,
and suitable to begin use in its own evolutionary
context.
|
Parameter |
defaultBase()
Returns the default base for this prototype.
|
int |
numSources()
Returns the number of sources to this pipeline.
|
int |
produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Produces n individuals from the given subpopulation
and puts them into inds[start...start+n-1],
where n = Min(Max(q,min),max), where q is the "typical" number of
individuals the BreedingSource produces in one shot, and returns
n.
|
void |
setup(EvolutionState state,
Parameter base)
Sets up the BreedingPipeline.
|
producesfinishProducing, individualReplaced, maxChildProduction, minChildProduction, preparePipeline, prepareToProduce, reproduce, sourcesAreProperForm, typicalIndsProducedgetProbability, pickRandom, setProbability, setupProbabilitiespublic static final java.lang.String P_MUTATEALLNODES
public static final int NUM_SOURCES
public GPNodeSelector nodeselect
public Parameter defaultBase()
Prototypepublic int numSources()
BreedingPipelinenumSources in class BreedingPipelinepublic java.lang.Object clone()
PrototypeTypically this should be a full "deep" clone. However, you may share certain elements with other objects rather than clone hem, depending on the situation:
Implementations.
public Object clone()
{
try
{
return super.clone();
}
catch ((CloneNotSupportedException e)
{ throw new InternalError(); } // never happens
}
public Object clone()
{
try
{
MyObject myobj = (MyObject) (super.clone());
// put your deep-cloning code here...
}
catch ((CloneNotSupportedException e)
{ throw new InternalError(); } // never happens
return myobj;
}
public Object clone()
{
MyObject myobj = (MyObject) (super.clone());
// put your deep-cloning code here...
return myobj;
}
clone in interface Prototypeclone in class BreedingPipelinepublic void setup(EvolutionState state, Parameter base)
BreedingSourceThe most common modification is to normalize it with some other set of probabilities, then set all of them up in increasing summation; this allows the use of the fast static BreedingSource-picking utility method, BreedingSource.pickRandom(...). In order to use this method, for example, if four breeding source probabilities are {0.3, 0.2, 0.1, 0.4}, then they should get normalized and summed by the outside owners as: {0.3, 0.5, 0.6, 1.0}.
setup in interface Prototypesetup in interface Setupsetup in class BreedingPipelinePrototype.setup(EvolutionState,Parameter)public int produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
BreedingSourceproduce in class BreedingSource